CIS 2033: Computational Probability and Statistics, Spring 2014

Lectures: Monday and Friday 12:30-13:50  TL 401A

Lab: Wednesday 12:00-13:50  Wachman 200

 

Schedule and Homework Assignments

 

Matlab Assignments

 

Instructor

TA

 

The goal is to introduce students to hot and extremely useful topics in computational statistics with hands on experience. It provides a modern approach to probability and computational statistics with applications in data mining. Students will be able to immediately see their results with programming assignments in Matlab. Matlab is a leading programming language of scientific computing. It is broadly used in the industry and academia. No prior Matlab knowledge is required. The course offers a solid foundation for further courses in data mining, machine learning, artificial intelligence, robotics, computer vision, and in general in computational statistics and scientific computing. The course is composed of 3 hours lecture and 2 hours lab with programming assignments in Matlab.

Course books:

Dekking, F.M., Kraaikamp, C., Lopuhaa, H.P., Meester, L.E., A Modern Introduction to Probability and Statistics. Second Edition. Springer 2007

ISBN: 978-1-85233-896-1

datasets

 

Michael Baron. Probability and Statistics for Computer Scientists, second edition, CRC, 2014, ISBN-13: 978-1-4822-1411-6

 

Course topics:

  1. Introduction
  2. Probability Concepts
  3. Sampling Concepts
  4. Generating Random Variables
  5. Exploratory Data Analysis
  6. Finding Structure in Data
  7. Monte Carlo Methods in Inferential Statistics
  8. Data Partitioning
  9. Probability Density Estimation
  10. Supervised Learning
  11. Unsupervised Learning
  12. Parametric Models
  13. Nonparametric Models
  14. Markov Chain Monte Carlo Methods

Prerequisites:

CIS 1068 (or CIS 1073) and Math 1041 (or Math 1031) with grades of C or better

 

Questions, email: latecki@temple.edu

 

Resources:

Maximum Likelihood Estimation Primer

Hidden Markov Model (HMM)

Viterbi Algorithm for HMM

Video Lectures by Sam Roweis

 

 

Exams, Project and Grading

Honor Code

Disabilities

I encourage students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.